CIOs entered the year with ambitions to bring more AI into their companies and start using AI to transform how work gets done. At the same time they also wanted to leverage tech to cut costs and boost productivity throughout the business.
CIOs entered the year with ambitions to bring more AI into their companies and start using AI to transform how work gets done. At the same time they also wanted to leverage tech to cut costs and boost productivity throughout the business.
Now into the fourth quarter, CIOs say they’re still pursuing those objectives and plenty more. They say they still have much work to do, and they’re aiming to close the year with wins in myriad areas.
Here, IT leaders share advice on what their peers should focus on based on what they are racing to finish before year’s end.
One of the top to-do items for many CIOs as they near year’s end is delivering on their promises.
As Robert Field, global CIO at tech advisory firm Adler Advisors, says, people may not remember all the work IT did in any given year, “but they’ll always remember the things you didn’t do that you said you would.”
Field also cites fiscal reasons for his drive to finish up all IT work planned for 2026, noting that the company, which runs on a calendar year, has already allocated the money for the work in its annual budget and has pegged some of it to anticipated tax credits.
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: CIO
- Published: 13/10/2026
Continue reading at the original source →
Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
I measure AI maturity with an uncomfortable question: can anyone explain, in one line, what decision the model makes and who answers if it gets it wrong? Where that is clear, the project moves. Where it is not, it stays in permanent pilot and gets cancelled with the excuse that the technology was not ready.
What usually goes wrong
The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.
What to watch
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
How I read this entry
My practical advice is to fix on day one what happens when the model is wrong: who reviews, how often, and at what point it gets switched off. It sounds defensive, but it is exactly what lets you be aggressive later, because the risk stopped being an unknown.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
No comments yet. Yours can be the first.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
